Systems and methods for dynamic aggregation of data and minimization of data loss

ABSTRACT

A computer-implemented system for dynamic aggregation of data and minimization of data loss is disclosed. The system may be configured to perform instructions for: aggregating information from a plurality of networked systems by collecting a set of data from the networked systems, the set of data comprising data associated with a predetermined period of time and comprising one or more central variables that are included in data associated with more than one networked systems of the plurality of networked systems and one or more associated variables that describe one or more aspects of the central variables; retrieving one or more data transformation rules based on a relational map among the central variables and the associated variables; and aggregating the first set of data into one or more master data structures corresponding to the central variables based on the data transformation rules.

TECHNICAL FIELD

The present disclosure generally relates to computerized methods andsystems for dynamically aggregating data from multiple networked systemsand minimizing data loss from unexpected events such as system outage.In particular, embodiments of the present disclosure relate to inventiveand unconventional systems that aggregate data from multiple networkedsystems that use different data types and formats. The aggregated dataare reconciled into a proprietary format that enables near real-timeaccess to arbitrary combinations of data and minimizes data loss.

BACKGROUND

Advancement of information technology has a wide implementation ofnetworked systems where businesses of various sizes utilize multiplecomputing systems to facilitate their operations. The use can range fromsimple event logging to database management and analytics. As moreoperations are computerized and businesses grow in size, the volume ofavailable data is rapidly increasing and frequently overwhelming.Moreover, different networked systems may use different data types andformats, making it difficult for business owners and managers tounderstand the vast amount of data and make appropriate decisions.Effective and efficient management of such vast amount of data canprovide significant competitive advantage.

Another complicating factor arises in collection and storage of suchdata, where networked systems are susceptible to unexpected problemssuch as network-wide outages or system level failures. Thesecircumstances are detrimental to the collection and storage of data fromthe networked systems because network outages can prevent all data frombeing transferred from one system to another and system level failurescan result in data loss until the problems are resolved. Prior artsystems have not been able to account for such failures, skipping dataaggregation if a networked system is unavailable or being unable toresume aggregation from the last successful aggregation.

Still further, the data collection is also without merit if the datacollection and analysis occur over a long period of time. “A longperiod” is a relative term where even a 10-minute delay in thecollection and analysis may be too long in some circumstances whileother systems may be okay with collecting data only once per day. Asbusiness operations advance to require more rapid responses, however, areal-time or a near real-time data collection and analysis become moreimportant.

Therefore, there is a need for dynamic aggregation of data in nearreal-time from different networked systems that can collect data ofdifferent formats and types, reconciling them to a single format tosupport sophisticated analysis, while being robust enough to account forunexpected problems and resume collection once the problems areresolved.

SUMMARY

One aspect of the present disclosure is directed to acomputer-implemented system for dynamic aggregation of data andminimization of data loss. The system may comprise a memory storinginstructions; and at least one processor configured to execute theinstructions. The instructions may comprise: aggregating informationfrom a plurality of networked systems by collecting a first set of dataat a first time point from the networked systems, the first set of datacomprising data associated with a predetermined period of time andcomprising one or more central variables that are included in dataassociated with more than one networked systems of the plurality ofnetworked systems and one or more associated variables that describe oneor more aspects of the central variables, each of the central variablesand the associated variables comprising a corresponding value;retrieving one or more data transformation rules based on a relationalmap among the central variables and the associated variables; andaggregating the first set of data into one or more master datastructures corresponding to the central variables based on the datatransformation rules, each of the one or more master data structurescomprising one or more data fields that correspond to one of the centralvariables and a subset of the associated variables; and generating oneor more data reports based on the master data structures.

Yet another aspect of the present disclosure is directed to acomputer-implemented method for dynamic aggregation of data andminimization of data loss. The method may comprise steps for aggregatinginformation from a plurality of networked systems by: collecting a firstset of data at a first time point from the networked systems, the firstset of data comprising data associated with a predetermined period oftime and comprising one or more central variables that are included indata associated with more than one networked systems of the plurality ofnetworked systems and one or more associated variables that describe oneor more aspects of the central variables, each of the central variablesand the associated variables comprising a corresponding value;retrieving one or more data transformation rules based on a relationalmap among the central variables and the associated variables; andaggregating the first set of data into one or more master datastructures corresponding to the central variables based on the datatransformation rules, each of the one or more master data structurescomprising one or more data fields that correspond to one of the centralvariables and a subset of the associated variables; and generating oneor more data reports based on the master data structures.

Furthermore, another aspect of the present disclosure is directed to acomputer-implemented system for dynamic aggregation of data andminimization of data loss. The system may comprise a memory storinginstructions; and at least one processor configured to execute theinstructions. The instructions may comprise aggregating information froma plurality of networked systems by: transmitting a data request to thenetworked systems at a predetermined interval; receiving a first set ofdata at a first time point from a first subset of networked systems, thefirst subset of networked systems having a first set of correspondingtimestamps from an immediately preceding time point, and the first setof data comprising data associated with a predetermined period of time;receiving a second set of data at the first time point from a secondsubset of networked systems, the second subset of networked systemshaving a second set of corresponding timestamps from a second time pointolder than the immediately preceding time point, and the second set ofdata comprising data associated with a period between the second timepoint and the first time point; receiving a third set of data at thefirst time point from a third subset of networked systems, the thirdsubset of networked systems having a third set of correspondingtimestamps from the immediately preceding time point, and the third setof data indicating that the third subset of networked systems are notavailable, wherein the first set of data and the second set of datacomprise one or more central variables that are included in data frommore than one networked systems of the plurality of networked systemsand one or more associated variables that describe one or more aspectsof the central variables; retrieving one or more data transformationrules based on a relational map among the central variables and theassociated variables; aggregating the first and second sets of data intoone or more master data structures corresponding to the centralvariables based on the data transformation rules, each of the one ormore master data structures comprising one or more data fields thatcorrespond to one of the central variables and a subset of theassociated variables; and updating the first and second sets ofcorresponding timestamps based on the first time point; and generatingone or more data reports based on the master data structures.

Other systems, methods, and computer-readable media are also discussedherein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a schematic block diagram illustrating an exemplaryembodiment of a network comprising computerized systems forcommunications enabling shipping, transportation, and logisticsoperations, consistent with the disclosed embodiments.

FIG. 1B depicts a sample Search Result Page (SRP) that includes one ormore search results satisfying a search request along with interactiveuser interface elements, consistent with the disclosed embodiments.

FIG. 1C depicts a sample Single Display Page (SDP) that includes aproduct and information about the product along with interactive userinterface elements, consistent with the disclosed embodiments.

FIG. 1D depicts a sample Cart page that includes items in a virtualshopping cart along with interactive user interface elements, consistentwith the disclosed embodiments.

FIG. 1E depicts a sample Order page that includes items from the virtualshopping cart along with information regarding purchase and shipping,along with interactive user interface elements, consistent with thedisclosed embodiments.

FIG. 2 is a diagrammatic illustration of an exemplary fulfillment centerconfigured to utilize disclosed computerized systems, consistent withthe disclosed embodiments.

FIG. 3 depicts a schematic block diagram illustrating an exemplaryembodiment of a networked environment comprising computerized systemsfor aggregating data and minimizing data loss, consistent with thedisclosed embodiments.

FIG. 4 depicts a flowchart of an exemplary computerized process foraggregating data from a plurality of networked systems, consistent withthe disclosed embodiments.

FIG. 5 depicts a flowchart of an extended exemplary computerized processfor aggregating data from a plurality of networked systems withadditional steps for minimizing data loss, consistent with the disclosedembodiments.

FIGS. 6A and 6B depict exemplary timelines of timestamps for differentnetworked systems before and after data aggregation, consistent with thedisclosed embodiments.

DETAILED DESCRIPTION

The following detailed description refers to the accompanying drawings.Wherever possible, the same reference numbers are used in the drawingsand the following description to refer to the same or similar parts.While several illustrative embodiments are described herein,modifications, adaptations and other implementations are possible. Forexample, substitutions, additions, or modifications may be made to thecomponents and steps illustrated in the drawings, and the illustrativemethods described herein may be modified by substituting, reordering,removing, or adding steps to the disclosed methods. Accordingly, thefollowing detailed description is not limited to the disclosedembodiments and examples. Instead, the proper scope of the invention isdefined by the appended claims.

Embodiments of the present disclosure are directed to systems andmethods for dynamic aggregation of data and minimization of data loss.

Referring to FIG. 1A, a schematic block diagram 100 illustrating anexemplary embodiment of a system comprising computerized systems forcommunications enabling shipping, transportation, and logisticsoperations is shown. As illustrated in FIG. 1A, system 100 may include avariety of systems, each of which may be connected to one another viaone or more networks. The systems may also be connected to one anothervia a direct connection, for example, using a cable. The depictedsystems include a shipment authority technology (SAT) system 101, anexternal front end system 103, an internal front end system 105, atransportation system 107, mobile devices 107A, 107B, and 107C, sellerportal 109, shipment and order tracking (SOT) system 111, fulfillmentoptimization (FO) system 113, fulfillment messaging gateway (FMG) 115,supply chain management (SCM) system 117, workforce management system119, mobile devices 119A, 119B, and 119C (depicted as being inside offulfillment center (FC) 200), 3^(rd) party fulfillment systems 121A,121B, and 121C, fulfillment center authorization system (FC Auth) 123,and labor management system (LMS) 125.

SAT system 101, in some embodiments, may be implemented as a computersystem that monitors order status and delivery status. For example, SATsystem 101 may determine whether an order is past its Promised DeliveryDate (PDD) and may take appropriate action, including initiating a neworder, reshipping the items in the non-delivered order, canceling thenon-delivered order, initiating contact with the ordering customer, orthe like. SAT system 101 may also monitor other data, including output(such as a number of packages shipped during a particular time period)and input (such as the number of empty cardboard boxes received for usein shipping). SAT system 101 may also act as a gateway between differentdevices in system 100, enabling communication (e.g., usingstore-and-forward or other techniques) between devices such as externalfront end system 103 and FO system 113.

External front end system 103, in some embodiments, may be implementedas a computer system that enables external users to interact with one ormore systems in system 100. For example, in embodiments where system 100enables the presentation of systems to enable users to place an orderfor an item, external front end system 103 may be implemented as a webserver that receives search requests, presents item pages, and solicitspayment information. For example, external front end system 103 may beimplemented as a computer or computers running software such as theApache HTTP Server, Microsoft Internet Information Services (IIS),NGINX, or the like. In other embodiments, external front end system 103may run custom web server software designed to receive and processrequests from external devices (e.g., mobile device 102A or computer102B), acquire information from databases and other data stores based onthose requests, and provide responses to the received requests based onacquired information.

In some embodiments, external front end system 103 may include one ormore of a web caching system, a database, a search system, or a paymentsystem. In one aspect, external front end system 103 may comprise one ormore of these systems, while in another aspect, external front endsystem 103 may comprise interfaces (e.g., server-to-server,database-to-database, or other network connections) connected to one ormore of these systems.

An illustrative set of steps, illustrated by FIGS. 1B, 1C, 1D, and 1E,will help to describe some operations of external front end system 103.External front end system 103 may receive information from systems ordevices in system 100 for presentation and/or display. For example,external front end system 103 may host or provide one or more web pages,including a Search Result Page (SRP) (e.g., FIG. 1B), a Single DetailPage (SDP) (e.g., FIG. 1C), a Cart page (e.g., FIG. 1D), or an Orderpage (e.g., FIG. 1E). A user device (e.g., using mobile device 102A orcomputer 102B) may navigate to external front end system 103 and requesta search by entering information into a search box. External front endsystem 103 may request information from one or more systems in system100. For example, external front end system 103 may request informationfrom FO System 113 that satisfies the search request. External front endsystem 103 may also request and receive (from FO System 113) a PromisedDelivery Date or “PDD” for each product included in the search results.The PDD, in some embodiments, may represent an estimate of when apackage containing the product will arrive at the user's desiredlocation or a date by which the product is promised to be delivered atthe user's desired location if ordered within a particular period oftime, for example, by the end of the day (11:59 PM). (PDD is discussedfurther below with respect to FO System 113.)

External front end system 103 may prepare an SRP (e.g., FIG. 1B) basedon the information. The SRP may include information that satisfies thesearch request. For example, this may include pictures of products thatsatisfy the search request. The SRP may also include respective pricesfor each product, or information relating to enhanced delivery optionsfor each product, PDD, weight, size, offers, discounts, or the like.External front end system 103 may send the SRP to the requesting userdevice (e.g., via a network).

A user device may then select a product from the SRP, e.g., by clickingor tapping a user interface, or using another input device, to select aproduct represented on the SRP. The user device may formulate a requestfor information on the selected product and send it to external frontend system 103. In response, external front end system 103 may requestinformation related to the selected product. For example, theinformation may include additional information beyond that presented fora product on the respective SRP. This could include, for example, shelflife, country of origin, weight, size, number of items in package,handling instructions, or other information about the product. Theinformation could also include recommendations for similar products(based on, for example, big data and/or machine learning analysis ofcustomers who bought this product and at least one other product),answers to frequently asked questions, reviews from customers,manufacturer information, pictures, or the like.

External front end system 103 may prepare an SDP (Single Detail Page)(e.g., FIG. 1C) based on the received product information. The SDP mayalso include other interactive elements such as a “Buy Now” button, a“Add to Cart” button, a quantity field, a picture of the item, or thelike. The SDP may further include a list of sellers that offer theproduct. The list may be ordered based on the price each seller offerssuch that the seller that offers to sell the product at the lowest pricemay be listed at the top. The list may also be ordered based on theseller ranking such that the highest ranked seller may be listed at thetop. The seller ranking may be formulated based on multiple factors,including, for example, the seller's past track record of meeting apromised PDD. External front end system 103 may deliver the SDP to therequesting user device (e.g., via a network).

The requesting user device may receive the SDP which lists the productinformation. Upon receiving the SDP, the user device may then interactwith the SDP. For example, a user of the requesting user device mayclick or otherwise interact with a “Place in Cart” button on the SDP.This adds the product to a shopping cart associated with the user. Theuser device may transmit this request to add the product to the shoppingcart to external front end system 103.

External front end system 103 may generate a Cart page (e.g., FIG. 1D).The Cart page, in some embodiments, lists the products that the user hasadded to a virtual “shopping cart.” A user device may request the Cartpage by clicking on or otherwise interacting with an icon on the SRP,SDP, or other pages. The Cart page may, in some embodiments, list allproducts that the user has added to the shopping cart, as well asinformation about the products in the cart such as a quantity of eachproduct, a price for each product per item, a price for each productbased on an associated quantity, information regarding PDD, a deliverymethod, a shipping cost, user interface elements for modifying theproducts in the shopping cart (e.g., deletion or modification of aquantity), options for ordering other product or setting up periodicdelivery of products, options for setting up interest payments, userinterface elements for proceeding to purchase, or the like. A user at auser device may click on or otherwise interact with a user interfaceelement (e.g., a button that reads “Buy Now”) to initiate the purchaseof the product in the shopping cart. Upon doing so, the user device maytransmit this request to initiate the purchase to external front endsystem 103.

External front end system 103 may generate an Order page (e.g., FIG. 1E)in response to receiving the request to initiate a purchase. The Orderpage, in some embodiments, re-lists the items from the shopping cart andrequests input of payment and shipping information. For example, theOrder page may include a section requesting information about thepurchaser of the items in the shopping cart (e.g., name, address, e-mailaddress, phone number), information about the recipient (e.g., name,address, phone number, delivery information), shipping information(e.g., speed/method of delivery and/or pickup), payment information(e.g., credit card, bank transfer, check, stored credit), user interfaceelements to request a cash receipt (e.g., for tax purposes), or thelike. External front end system 103 may send the Order page to the userdevice.

The user device may enter information on the Order page and click orotherwise interact with a user interface element that sends theinformation to external front end system 103. From there, external frontend system 103 may send the information to different systems in system100 to enable the creation and processing of a new order with theproducts in the shopping cart.

In some embodiments, external front end system 103 may be furtherconfigured to enable sellers to transmit and receive informationrelating to orders.

Internal front end system 105, in some embodiments, may be implementedas a computer system that enables internal users (e.g., employees of anorganization that owns, operates, or leases system 100) to interact withone or more systems in system 100. For example, in embodiments wherenetwork 101 enables the presentation of systems to enable users to placean order for an item, internal front end system 105 may be implementedas a web server that enables internal users to view diagnostic andstatistical information about orders, modify item information, or reviewstatistics relating to orders. For example, internal front end system105 may be implemented as a computer or computers running software suchas the Apache HTTP Server, Microsoft Internet Information Services(IIS), NGINX, or the like. In other embodiments, internal front endsystem 105 may run custom web server software designed to receive andprocess requests from systems or devices depicted in system 100 (as wellas other devices not depicted), acquire information from databases andother data stores based on those requests, and provide responses to thereceived requests based on acquired information.

In some embodiments, internal front end system 105 may include one ormore of a web caching system, a database, a search system, a paymentsystem, an analytics system, an order monitoring system, or the like. Inone aspect, internal front end system 105 may comprise one or more ofthese systems, while in another aspect, internal front end system 105may comprise interfaces (e.g., server-to-server, database-to-database,or other network connections) connected to one or more of these systems.

Transportation system 107, in some embodiments, may be implemented as acomputer system that enables communication between systems or devices insystem 100 and mobile devices 107A-107C. Transportation system 107, insome embodiments, may receive information from one or more mobiledevices 107A-107C (e.g., mobile phones, smart phones, PDAs, or thelike). For example, in some embodiments, mobile devices 107A-107C maycomprise devices operated by delivery workers. The delivery workers, whomay be permanent, temporary, or shift employees, may utilize mobiledevices 107A-107C to effect delivery of packages containing the productsordered by users. For example, to deliver a package, the delivery workermay receive a notification on a mobile device indicating which packageto deliver and where to deliver it. Upon arriving at the deliverylocation, the delivery worker may locate the package (e.g., in the backof a truck or in a crate of packages), scan or otherwise capture dataassociated with an identifier on the package (e.g., a barcode, an image,a text string, an RFID tag, or the like) using the mobile device, anddeliver the package (e.g., by leaving it at a front door, leaving itwith a security guard, handing it to the recipient, or the like). Insome embodiments, the delivery worker may capture photo(s) of thepackage and/or may obtain a signature using the mobile device. Themobile device may send information to transportation system 107including information about the delivery, including, for example, time,date, GPS location, photo(s), an identifier associated with the deliveryworker, an identifier associated with the mobile device, or the like.Transportation system 107 may store this information in a database (notpictured) for access by other systems in system 100. Transportationsystem 107 may, in some embodiments, use this information to prepare andsend tracking data to other systems indicating the location of aparticular package.

In some embodiments, certain users may use one kind of mobile device(e.g., permanent workers may use a specialized PDA with custom hardwaresuch as a barcode scanner, stylus, and other devices) while other usersmay use other kinds of mobile devices (e.g., temporary or shift workersmay utilize off-the-shelf mobile phones and/or smartphones).

In some embodiments, transportation system 107 may associate a user witheach device. For example, transportation system 107 may store anassociation between a user (represented by, e.g., a user identifier, anemployee identifier, or a phone number) and a mobile device (representedby, e.g., an International Mobile Equipment Identity (IMEI), anInternational Mobile Subscription Identifier (IMSI), a phone number, aUniversal Unique Identifier (UUID), or a Globally Unique Identifier(GUID)). Transportation system 107 may use this association inconjunction with data received on deliveries to analyze data stored inthe database in order to determine, among other things, a location ofthe worker, an efficiency of the worker, or a speed of the worker.

Seller portal 109, in some embodiments, may be implemented as a computersystem that enables sellers or other external entities to electronicallycommunicate with one or more systems in system 100. For example, aseller may utilize a computer system (not pictured) to upload or provideproduct information, order information, contact information, or thelike, for products that the seller wishes to sell through system 100using seller portal 109.

Shipment and order tracking system 111, in some embodiments, may beimplemented as a computer system that receives, stores, and forwardsinformation regarding the location of packages containing productsordered by customers (e.g., by a user using devices 102A-102B). In someembodiments, shipment and order tracking system 111 may request or storeinformation from web servers (not pictured) operated by shippingcompanies that deliver packages containing products ordered bycustomers.

In some embodiments, shipment and order tracking system 111 may requestand store information from systems depicted in system 100. For example,shipment and order tracking system 111 may request information fromtransportation system 107. As discussed above, transportation system 107may receive information from one or more mobile devices 107A-107C (e.g.,mobile phones, smart phones, PDAs, or the like) that are associated withone or more of a user (e.g., a delivery worker) or a vehicle (e.g., adelivery truck). In some embodiments, shipment and order tracking system111 may also request information from workforce management system (WMS)119 to determine the location of individual products inside of afulfillment center (e.g., fulfillment center 200). Shipment and ordertracking system 111 may request data from one or more of transportationsystem 107 or WMS 119, process it, and present it to a device (e.g.,user devices 102A and 102B) upon request.

Fulfillment optimization (FO) system 113, in some embodiments, may beimplemented as a computer system that stores information for customerorders from other systems (e.g., external front end system 103 and/orshipment and order tracking system 111). FO system 113 may also storeinformation describing where particular items are held or stored. Forexample, certain items may be stored only in one fulfillment center,while certain other items may be stored in multiple fulfillment centers.In still other embodiments, certain fulfillment centers may be designedto store only a particular set of items (e.g., fresh produce or frozenproducts). FO system 113 stores this information as well as associatedinformation (e.g., quantity, size, date of receipt, expiration date,etc.).

FO system 113 may also calculate a corresponding PDD (promised deliverydate) for each product. The PDD, in some embodiments, may be based onone or more factors. For example, FO system 113 may calculate a PDD fora product based on a past demand for a product (e.g., how many timesthat product was ordered during a period of time), an expected demandfor a product (e.g., how many customers are forecast to order theproduct during an upcoming period of time), a network-wide past demandindicating how many products were ordered during a period of time, anetwork-wide expected demand indicating how many products are expectedto be ordered during an upcoming period of time, one or more counts ofthe product stored in each fulfillment center 200, which fulfillmentcenter stores each product, expected or current orders for that product,or the like.

In some embodiments, FO system 113 may determine a PDD for each producton a periodic basis (e.g., hourly) and store it in a database forretrieval or sending to other systems (e.g., external front end system103, SAT system 101, shipment and order tracking system 111). In otherembodiments, FO system 113 may receive electronic requests from one ormore systems (e.g., external front end system 103, SAT system 101,shipment and order tracking system 111) and calculate the PDD on demand.

Fulfilment messaging gateway (FMG) 115, in some embodiments, may beimplemented as a computer system that receives a request or response inone format or protocol from one or more systems in system 100, such asFO system 113, converts it to another format or protocol, and forward itin the converted format or protocol to other systems, such as WMS 119 or3^(rd) party fulfillment systems 121A, 121B, or 121C, and vice versa.

Supply chain management (SCM) system 117, in some embodiments, may beimplemented as a computer system that performs forecasting functions.For example, SCM system 117 may forecast a level of demand for aparticular product based on, for example, based on a past demand forproducts, an expected demand for a product, a network-wide past demand,a network-wide expected demand, a count products stored in eachfulfillment center 200, expected or current orders for each product, orthe like. In response to this forecasted level and the amount of eachproduct across all fulfillment centers, SCM system 117 may generate oneor more purchase orders to purchase and stock a sufficient quantity tosatisfy the forecasted demand for a particular product.

Workforce management system (WMS) 119, in some embodiments, may beimplemented as a computer system that monitors workflow. For example,WMS 119 may receive event data from individual devices (e.g., devices107A-107C or 119A-119C) indicating discrete events. For example, WMS 119may receive event data indicating the use of one of these devices toscan a package. As discussed below with respect to fulfillment center200 and FIG. 2, during the fulfillment process, a package identifier(e.g., a barcode or RFID tag data) may be scanned or read by machines atparticular stages (e.g., automated or handheld barcode scanners, RFIDreaders, high-speed cameras, devices such as tablet 119A, mobiledevice/PDA 1198, computer 119C, or the like). WMS 119 may store eachevent indicating a scan or a read of a package identifier in acorresponding database (not pictured) along with the package identifier,a time, date, location, user identifier, or other information, and mayprovide this information to other systems (e.g., shipment and ordertracking system 111).

WMS 119, in some embodiments, may store information associating one ormore devices (e.g., devices 107A-107C or 119A-119C) with one or moreusers associated with system 100. For example, in some situations, auser (such as a part- or full-time employee) may be associated with amobile device in that the user owns the mobile device (e.g., the mobiledevice is a smartphone). In other situations, a user may be associatedwith a mobile device in that the user is temporarily in custody of themobile device (e.g., the user checked the mobile device out at the startof the day, will use it during the day, and will return it at the end ofthe day).

WMS 119, in some embodiments, may maintain a work log for each userassociated with system 100. For example, WMS 119 may store informationassociated with each employee, including any assigned processes (e.g.,unloading trucks, picking items from a pick zone, rebin wall work,packing items), a user identifier, a location (e.g., a floor or zone ina fulfillment center 200), a number of units moved through the system bythe employee (e.g., number of items picked, number of items packed), anidentifier associated with a device (e.g., devices 119A-119C), or thelike. In some embodiments, WMS 119 may receive check-in and check-outinformation from a timekeeping system, such as a timekeeping systemoperated on a device 119A-119C.

3^(rd) party fulfillment (3PL) systems 121A-121C, in some embodiments,represent computer systems associated with third-party providers oflogistics and products. For example, while some products are stored infulfillment center 200 (as discussed below with respect to FIG. 2),other products may be stored off-site, may be produced on demand, or maybe otherwise unavailable for storage in fulfillment center 200. 3PLsystems 121A-121C may be configured to receive orders from FO system 113(e.g., through FMG 115) and may provide products and/or services (e.g.,delivery or installation) to customers directly. In some embodiments,one or more of 3PL systems 121A-121C may be part of system 100, while inother embodiments, one or more of 3PL systems 121A-121C may be outsideof system 100 (e.g., owned or operated by a third-party provider).

Fulfillment Center Auth system (FC Auth) 123, in some embodiments, maybe implemented as a computer system with a variety of functions. Forexample, in some embodiments, FC Auth 123 may act as a single-sign on(SSO) service for one or more other systems in system 100. For example,FC Auth 123 may enable a user to log in via internal front end system105, determine that the user has similar privileges to access resourcesat shipment and order tracking system 111, and enable the user to accessthose privileges without requiring a second log in process. FC Auth 123,in other embodiments, may enable users (e.g., employees) to associatethemselves with a particular task. For example, some employees may nothave an electronic device (such as devices 119A-119C) and may insteadmove from task to task, and zone to zone, within a fulfillment center200, during the course of a day. FC Auth 123 may be configured to enablethose employees to indicate what task they are performing and what zonethey are in at different times of day.

Labor management system (LMS) 125, in some embodiments, may beimplemented as a computer system that stores attendance and overtimeinformation for employees (including full-time and part-time employees).For example, LMS 125 may receive information from FC Auth 123, WMA 119,devices 119A-119C, transportation system 107, and/or devices 107A-107C.

The particular configuration depicted in FIG. 1A is an example only. Forexample, while FIG. 1A depicts FC Auth system 123 connected to FO system113, not all embodiments require this particular configuration. Indeed,in some embodiments, the systems in system 100 may be connected to oneanother through one or more public or private networks, including theInternet, an Intranet, a WAN (Wide-Area Network), a MAN(Metropolitan-Area Network), a wireless network compliant with the IEEE802.11a/b/g/n Standards, a leased line, or the like. In someembodiments, one or more of the systems in system 100 may be implementedas one or more virtual servers implemented at a data center, serverfarm, or the like.

FIG. 2 depicts a fulfillment center 200. Fulfillment center 200 is anexample of a physical location that stores items for shipping tocustomers when ordered. Fulfillment center (FC) 200 may be divided intomultiple zones, each of which are depicted in FIG. 2. These “zones,” insome embodiments, may be thought of as virtual divisions betweendifferent stages of a process of receiving items, storing the items,retrieving the items, and shipping the items. So while the “zones” aredepicted in FIG. 2, other divisions of zones are possible, and the zonesin FIG. 2 may be omitted, duplicated, or modified in some embodiments.

Inbound zone 203 represents an area of FC 200 where items are receivedfrom sellers who wish to sell products using system 100 from FIG. 1A.For example, a seller may deliver items 202A and 202B using truck 201.Item 202A may represent a single item large enough to occupy its ownshipping pallet, while item 202B may represent a set of items that arestacked together on the same pallet to save space.

A worker will receive the items in inbound zone 203 and may optionallycheck the items for damage and correctness using a computer system (notpictured). For example, the worker may use a computer system to comparethe quantity of items 202A and 202B to an ordered quantity of items. Ifthe quantity does not match, that worker may refuse one or more of items202A or 2028. If the quantity does match, the worker may move thoseitems (using, e.g., a dolly, a handtruck, a forklift, or manually) tobuffer zone 205. Buffer zone 205 may be a temporary storage area foritems that are not currently needed in the picking zone, for example,because there is a high enough quantity of that item in the picking zoneto satisfy forecasted demand. In some embodiments, forklifts 206 operateto move items around buffer zone 205 and between inbound zone 203 anddrop zone 207. If there is a need for items 202A or 202B in the pickingzone (e.g., because of forecasted demand), a forklift may move items202A or 202B to drop zone 207.

Drop zone 207 may be an area of FC 200 that stores items before they aremoved to picking zone 209. A worker assigned to the picking task (a“picker”) may approach items 202A and 202B in the picking zone, scan abarcode for the picking zone, and scan barcodes associated with items202A and 202B using a mobile device (e.g., device 119B). The picker maythen take the item to picking zone 209 (e.g., by placing it on a cart orcarrying it).

Picking zone 209 may be an area of FC 200 where items 208 are stored onstorage units 210. In some embodiments, storage units 210 may compriseone or more of physical shelving, bookshelves, boxes, totes,refrigerators, freezers, cold stores, or the like. In some embodiments,picking zone 209 may be organized into multiple floors. In someembodiments, workers or machines may move items into picking zone 209 inmultiple ways, including, for example, a forklift, an elevator, aconveyor belt, a cart, a handtruck, a dolly, an automated robot ordevice, or manually. For example, a picker may place items 202A and 202Bon a handtruck or cart in drop zone 207 and walk items 202A and 202B topicking zone 209.

A picker may receive an instruction to place (or “stow”) the items inparticular spots in picking zone 209, such as a particular space on astorage unit 210. For example, a picker may scan item 202A using amobile device (e.g., device 119B). The device may indicate where thepicker should stow item 202A, for example, using a system that indicatean aisle, shelf, and location. The device may then prompt the picker toscan a barcode at that location before stowing item 202A in thatlocation. The device may send (e.g., via a wireless network) data to acomputer system such as WMS 119 in FIG. 1A indicating that item 202A hasbeen stowed at the location by the user using device 1198.

Once a user places an order, a picker may receive an instruction ondevice 1198 to retrieve one or more items 208 from storage unit 210. Thepicker may retrieve item 208, scan a barcode on item 208, and place iton transport mechanism 214. While transport mechanism 214 is representedas a slide, in some embodiments, transport mechanism may be implementedas one or more of a conveyor belt, an elevator, a cart, a forklift, ahandtruck, a dolly, a cart, or the like. Item 208 may then arrive atpacking zone 211.

Packing zone 211 may be an area of FC 200 where items are received frompicking zone 209 and packed into boxes or bags for eventual shipping tocustomers. In packing zone 211, a worker assigned to receiving items (a“rebin worker”) will receive item 208 from picking zone 209 anddetermine what order it corresponds to. For example, the rebin workermay use a device, such as computer 119C, to scan a barcode on item 208.Computer 119C may indicate visually which order item 208 is associatedwith. This may include, for example, a space or “cell” on a wall 216that corresponds to an order. Once the order is complete (e.g., becausethe cell contains all items for the order), the rebin worker mayindicate to a packing worker (or “packer”) that the order is complete.The packer may retrieve the items from the cell and place them in a boxor bag for shipping. The packer may then send the box or bag to a hubzone 213, e.g., via forklift, cart, dolly, handtruck, conveyor belt,manually, or otherwise.

Hub zone 213 may be an area of FC 200 that receives all boxes or bags(“packages”) from packing zone 211. Workers and/or machines in hub zone213 may retrieve package 218 and determine which portion of a deliveryarea each package is intended to go to, and route the package to anappropriate camp zone 215. For example, if the delivery area has twosmaller sub-areas, packages will go to one of two camp zones 215. Insome embodiments, a worker or machine may scan a package (e.g., usingone of devices 119A-119C) to determine its eventual destination. Routingthe package to camp zone 215 may comprise, for example, determining aportion of a geographical area that the package is destined for (e.g.,based on a postal code) and determining a camp zone 215 associated withthe portion of the geographical area.

Camp zone 215, in some embodiments, may comprise one or more buildings,one or more physical spaces, or one or more areas, where packages arereceived from hub zone 213 for sorting into routes and/or sub-routes. Insome embodiments, camp zone 215 is physically separate from FC 200 whilein other embodiments camp zone 215 may form a part of FC 200.

Workers and/or machines in camp zone 215 may determine which routeand/or sub-route a package 220 should be associated with, for example,based on a comparison of the destination to an existing route and/orsub-route, a calculation of workload for each route and/or sub-route,the time of day, a shipping method, the cost to ship the package 220, aPDD associated with the items in package 220, or the like. In someembodiments, a worker or machine may scan a package (e.g., using one ofdevices 119A-119C) to determine its eventual destination. Once package220 is assigned to a particular route and/or sub-route, a worker and/ormachine may move package 220 to be shipped. In exemplary FIG. 2, campzone 215 includes a truck 222, a car 226, and delivery workers 224A and224B. In some embodiments, truck 222 may be driven by delivery worker224A, where delivery worker 224A is a full-time employee that deliverspackages for FC 200 and truck 222 is owned, leased, or operated by thesame company that owns, leases, or operates FC 200. In some embodiments,car 226 may be driven by delivery worker 224B, where delivery worker224B is a “flex” or occasional worker that is delivering on an as-neededbasis (e.g., seasonally). Car 226 may be owned, leased, or operated bydelivery worker 224B.

FIG. 3 depicts a schematic block diagram illustrating an exemplaryembodiment of a networked environment 300 comprising computerizedsystems for aggregating data and minimizing data loss. Environment 300may include a variety of systems, each of which may be connected to oneanother via one or more networks. The systems may also be connected toone another via a direct connection, for example, using a cable. Thedepicted systems include networked systems A-D 310A-D, a data pipelinesystem (DPS) 320, and one or more client terminals 320.

Networked systems 310A-D, in some embodiments, may be implemented as oneor more computer systems that collect, accrue, and/or generate variousdata as part of their respective operations. For example, networkedsystems 310A-D may be similar in design, function, or operation to FOsystem 113, WMS 119, FC Auth 123, and LMS 125 of FIG. 1A, respectively.Alternatively, one or more of the networked systems 310A-D may beimplemented as one or more databases or memories configured to storedata collected, accrued, and/or generated by the respective computersystems. In some embodiments, such databases or memories may includecloud-based databases or on-premises databases. Also in someembodiments, such databases or memories may comprise one or more harddisk drives, one or more solid state drives, or one or morenon-transitory memories. While only four networked systems 310A-D aredepicted in FIG. 3, the number is only exemplary and networked systemsmay include any number of systems.

DPS 320, in some embodiments, may be implemented as a computer systemconfigured to dynamically aggregate data from networked systems 310A-Dthat allows a user to analyze the data in multiple perspectives (e.g.,performance over time, performance by zone, etc.). DPS 320 may also beconfigured to minimize data loss in the event of an unexpected systemfailure or a network failure by remembering exactly when it lastaggregated data from a networked system in the event of a failure andresuming the aggregation from the particular networked system once thefailure is resolved.

In some embodiments, DPS 320 comprises a data mapper 321, atransformation rule storage 322, a master data structure (MDS) storage323, a timestamp storage 324, a data aggregator 325, and a reportgenerator 326. In addition, DPS 320 may also comprise a front-end module327 that receives data analysis queries from client terminals 330 andtransmits outputs from report generator 326 to client terminals 330.

In some embodiments, DPS 320 may comprise one or more processors, one ormore memories, and one or more input/output (I/O) devices. DPS 320 maytake the form of a server, general-purpose computer, a mainframecomputer, a special-purpose computing device such as a graphicalprocessing unit (GPU), laptop, or any combination of these computingdevices. In these embodiments, components of DPS 320 (i.e., data mapper321, transformation rule storage 322, MDS storage 323, timestamp storage324, data aggregator 325, report generator 326, and front-end module327) may be implemented as one or more functional units performed by oneor more processors based on instructions stored in the one or morememories. DPS 320 may be a standalone system, or it may be part of asubsystem, which may be part of a larger system.

Alternatively, components of DPS 320 may be implemented as one or morecomputer systems communicating with each other via a network. In thisembodiment, each of the one or more computer systems may comprise one ormore processors, one or more memories (i.e., non-transitorycomputer-readable media), and one or more input/output (I/O) devices. Insome embodiments, each of the one or more computer systems may take theform of a server, general-purpose computer, a mainframe computer, aspecial-purpose computing device such as a GPU, laptop, or anycombination of these computing devices.

Data mapper 321, in some embodiments, may include one or more computingdevices configured to determine a relational map between one or morevariables included in the data retrieved from networked systems 310A-D.The relational map may define how different variables included in thedata from each networked systems 310A-D are related to each other. Insome embodiments, data mapper 321 may define the relational map based onhow one or more central variables that are associated with data frommore than one of the networked systems are related to one or moreassociated variables that describe certain aspects of the centralvariables.

For example, when networked systems 310A-D includes WMS 119 and LMS 125of FIG. 1A, data from the two systems may both contain workeridentifier, where data from WMS 119 may describe a series of event dataassociated with the worker identifier (e.g., worker X scanned productidentifier A for order identifier P at time T). Similarly data from LMS125 may describe attendance and overtime information associated with theworker identifier (e.g., worker X worked from time T1 to time T2 on dateD). Data mapper 321 may identify that the worker identifier is a centralvariable that can consolidate data from WMS 119 and LMS 125 andconsolidate the data to describe the worker based on other associatedvariables (e.g., time T1, T2, T, order identifier P, etc.). Additionallyor alternatively, data mapper 321 may identify order identifier as acentral variable and consolidate data using other variables to describethe corresponding order (e.g., order identifier P containing productidentifier A was scanned by worker identifier X at time T).

As described above, data mapper 321 may consolidate knowledge of thevariables as they pertain to a real-world operation and determine arelational map among all variables included in the data from thenetworked systems 310A-D. In some embodiments, data mapper 321 mayconsider data profiles specified by each of the networked systems310A-D, which may comprise information such as metadata, definitions ofthe variables, a data element synonym registry, and the like. The dataelement synonym registry may be a list of synonyms that may be used todescribe a particular variable, which can be used to identify related oridentical variables when different networked systems use different termsto describe similar variables.

In some embodiments, data mapper 321 may use the relational map togenerate a set of transformation rules that dictate how data fromnetwork systems 310A-D should be organized. In other words,transformation rules may dictate how each variable included in the datafrom network systems 310A-D should map to different data fields of oneor more MDSes described below. In some embodiments, data mapper 321 maytransmit and store the transformation rules in transformation rulestorage 322.

Data aggregator 325, in some embodiments, may include one or morecomputing devices configured to retrieve data from one or more networkedsystems 310A-D. Data aggregator 325 may than aggregate the retrieveddata into one or more MDSes based on transformation rules retrieved fromtransformation rule storage 322. Specifically, data aggregator 325 mayreceive a data from a particular networked system (e.g., 310A), identifyvariables therein, retrieve corresponding transformation rules fromtransformation rule storage 322, and assign values corresponding to eachvariable to one or more data fields in one or more MDSes based on thetransformation rules. In some embodiments, data aggregator 325 may alsoupdate timestamps stored in timestamp storage 324 that correspond toeach networked system.

Transformation rule storage 322, MDS storage 323, and timestamp storage324, in some embodiments, may include one or more databases or memoriesconfigured to store corresponding types of data. The three storage units(i.e., transformation rule storage 322, MDS storage 323, and timestampstorage 324), may also be implemented together as a single collection ofstorage devices, each occupying a portion of the single collection ofstorage devices. The three storage units may each include, or maycollectively include, cloud-based databases or on-premises databases. Insome embodiments, the three storage units may each include, or maycollectively include, one or more hard disk drives, one or more solidstate drives, or one or more non-transitory memories.

Report generator 326, in some embodiments, may include one or morecomputing devices configured to generate reports based on data analysisqueries received from client terminals 330 via front-end module 327. Thereports can range from simple ones that output last known aggregationtime points for the networked systems 310A-D based on the timestampsstored in timestamp storage 324 to complex ones that require calculationof performance history over time by a particular worker or by aparticular facility. In some embodiments, aggregation of data into MDSesmay enable report generator 326 to analyze the data in multipledimensions such as over time, by workers, by zones, by order, and thelike. Furthermore, report generator 326 may enable high-level analysesof performance metrics such as number of orders processed per hour,number of idle workers, and number of orders completed on time; anddetailed analyses such as average number of units processed per hour fortop 10% of workers.

Front-end module 327, in some embodiments, may be implemented as acomputer system that enables external users to interact with one or morecomponents of DPS 320. For example, in embodiments where DPS 320 enablesusers to submit a data analysis query, front-end module 327 may beimplemented as a web server that receives such queries and presentsoutcome of the analysis as discussed above. In some embodiments,front-end module 327 may be implemented as a computer or computersrunning software such as the Apache HTTP Server, Microsoft InternetInformation Services (IIS), NGINX, or the like. In other embodiments,front-end module 327 may run a custom web server software designed toreceive and process queries from client terminals 330, instruct othersystems to acquire information from databases, run analysis, and provideresponses to the received queries based on the acquired information.

Client terminals 330, in some embodiments, may include one or morecomputing devices configured to enable users (e.g., business owners orfacility operators) to access DPS 320 via front-end module 327. Clientterminals 330 may include any combination of computing devices such aspersonal computers, mobile phones, smartphones, PDAs, or the like. Insome embodiments, users may use client terminals 330 to access a webinterface provided by front-end module 327 and submit a query for dataanalysis. And once the data analysis is complete, users may use clientterminals 330 to receive outcomes via the web interface provided byfront-end module 327.

FIG. 4 depicts a flowchart of an exemplary computerized process 400 foraggregating data from a plurality of networked systems. In someembodiments, process 400 may be performed by data aggregator 325 usinginformation from other components of DPS 320 as described above.

FIG. 5 depicts a flowchart of an extended exemplary computerized process500 for aggregating data from a plurality of networked systems withadditional steps for minimizing data loss. In some embodiments, extendedprocess 500 may also be performed by data aggregator 325, similar toprocess 400, using information from other components of DPS 320 asdescribed above.

FIGS. 6A and 6B depict exemplary timelines 610 and 620 of timestamps fordifferent networked systems before and after data aggregation. Followingdescriptions of process 400 and extended process 500 will make referenceto FIGS. 6A and 6B for clarity.

Referring back to FIG. 4, data aggregator 325 may repeat steps 401-404at predetermined intervals (i.e., checkpoint interval 604 of FIG. 6A).At step 401, data aggregator 325 may begin one cycle of steps 401-404 bycollecting a set of data from networked systems 310A-D. This collectionof data may occur, for example, at a current time point 602. Tick marks601 on timelines 610 and 620 mark different time points (i.e.,checkpoints) over time. In some embodiments, data aggregator 325 mayretrieve data from each network system (e.g., 310A) that accumulatedover a predetermined period of time called regular collection window605, which may be equal to checkpoint interval 604 or longer by apredetermined overlap 607. In some embodiments, regular collectionwindow 605 may be equal to or longer than twice checkpoint interval 604.

In some embodiments, checkpoint interval 604 may be user selectableand/or may range anywhere from mere seconds or a fraction of a second tohours or days. A smaller checkpoint interval 604 may allow a more rapidrepetition of steps 401-404, which would result in a more frequent dataaggregation, allowing report generator 326 to provide more up-to-datedata analysis reports. In some embodiments, checkpoint interval 604 maybe sufficiently small to provide near real-time or real-time reports.

At step 402, data aggregator 325 may retrieve data transformation rulesfrom transformation rule storage 322 based on a relational map amongcentral variables and associated variables included in the data from thenetworked systems 310A-D. This may involve, in some embodiments,generating, by data aggregator 325, a list of variables included in thedata and querying transformation rule storage 322 to retrievetransformation rules associated with any of the variables.

At step 403, data aggregator 325 may aggregate the data into one or moremaster data structures (MDSes) corresponding to the central variablesbased on the retrieved transformation rules. In some embodiments, eachof the MDSes may comprise data fields that correspond to one of thecentral variables and a subset of the associated variables. For example,an MDS may comprise data fields corresponding to an order identifier, areceived date, a PDD, a status identifier, and the like. In otherembodiments, each of the MDSes may contain a central data field thatcorresponds to one of the central variables and contain additional datafields that correspond to any of the central or associated variables.For example, an MDS may comprise a central data field for an orderidentifier and a plurality of data fields for a received date, a PDD, astatus identifier, a worker identifier, and the like.

In this way, data aggregator 325 may, for each data retrieved fromnetworked systems 310A-D, assign values of variables stored therein toone or more corresponding data fields in one or more MDSes. For example,data aggregator 325 may assign a value for an order identifier includedin data from FO system 113 to a central data field in one MDS and a datafield in another MDS that use worker identifier as the central variable.

In some embodiments, aggregating the data into MDSes may comprisesorting the data from networked systems 310A-D based on time from oldestto latest and iterating through the sorted data chronologically toreplace existing values in data fields of the MDSes with the values fromthe data. In some instances where a data field is empty, data aggregator325 may simply assign the value from the data as a new value for thedata field.

At step 404, data aggregator 325 updates timestamps (e.g., timestamp A612 and timestamp C 614 for networked systems A and C 310A and 310C) fornetworked systems 310A-D. Timestamp storage 324 may store and keep trackof timestamps for each networked system 310A-D. Under normal operationwhere there has been no unexpected failure, all timestamps may be set toa time point immediately preceding current time point 602 during aprevious data aggregation, like timestamps A 612 and C 614 in FIG. 6A.After a successful data aggregation, data aggregator 325 may updatecorresponding timestamps to the current time point 602, like an updatedtimestamp A 622 and an updated timestamp C 624 in FIG. 6B.

Once data aggregation is complete, data aggregator 325 may wait untilthe next time point 603, a checkpoint interval in the future fromcurrent time point 602, and repeat steps 401-404. Additionally oralternatively, report generator 326 may, at step 405, generate datareports in response to data analysis queries submitted by a user viaclient terminal(s) 330 as described above.

Referring to FIG. 5, extended process 500 depicts more detail to process400 that allows DPS 320 to keep track of data aggregation with respectto each networked system and minimize data loss. Given a number ofnetworked systems 310A-D, the systems are always at risk of unexpectedfailures such as a network outage or a system-level failure. As such,networked systems 310A-D may be divided into three categories at anygiven moment: (1) those that completed data aggregation at theimmediately preceding checkpoint (normal networked systems), (2) thosethat had been unavailable for any number of reasons but are currentlyavailable for aggregation (i.e., repaired networked systems), and (3)those that are currently unavailable (unavailable networked systems).

At the beginning of a data aggregation cycle (e.g., steps 401-404 ofFIG. 4), data aggregator 325 may, at step 501, transmit a data requestto networked systems 310A-D. In response, data aggregator 325 mayreceive and collect three sets of data from the three categories ofnetworked systems 310A-D. Specifically, data aggregator 325 may receive,at step 502, a first set of data from normal networked systems (e.g.,310A and 310C); at step 503, a second set of data from repairednetworked systems (e.g., 310B); and at step 504, a third set of datafrom unavailable networked systems (e.g., 310D). In some embodiments,unavailable networked systems (e.g., 310D) may return nothing or atimeout exception. And in some embodiments, data aggregator 325 mayidentify different categories of networked systems 310A-D based on thereceived sets of data as described above and/or based on respectivetimestamps stored in timestamp storage 324. For example, the timestampscorresponding to normal networked systems (e.g., 310A and 310C) may beset at checkpoint A 612 and checkpoint C 614, respectively, immediatelypreceding current time point 602, and the timestamps corresponding tounavailable or repaired networked systems (e.g., 310D and 310B) may beset at any of the previous checkpoints such as checkpoint D 615 andcheckpoint B 613, respectively, because data aggregator 325 may havebeen unable to update them in a previous cycle, or it may have omittedthem to keep track of each networked system. To be clear, dataaggregator 325 may differentiate between unavailable and repairednetworked systems based on the collected data at step 508.

For normal and repaired networked systems (e.g., 310A, 310C, and 310B),data aggregator 325 aggregates data in a manner described above withrespect to FIG. 4. However, one of differences between the twocategories of networked systems is that data from normal networkedsystems comprise those that accrued during regular collection window 605as described above, while data from repaired networked systems comprisethose that accrued during an extended collection window 606. Extendedcollection window may span, at minimum, the period of time from acorresponding timestamp of the repaired networked system (e.g.,timestamp 613) to the current time point 602. In some embodiments,extended collection window may also comprise an amount of time equal topredetermined overlap 607.

After aggregating data from both categories of networked systems, dataaggregator may, at step 507, update corresponding timestamps to currenttime point 602. For example, data aggregator 325 may update timestamp A612 and timestamp C 614 corresponding to normal networked systems 310Aand 310C to updated timestamp A 622 and updated timestamp C 624,respectively; and update timestamp B 613 corresponding to repairednetworked system 310B to updated timestamp B 623.

On the other hand, timestamp (e.g., timestamp D 615) corresponding tounavailable networked systems (e.g., 310D) may be stuck at some timepoint in the past. And data aggregator 325 may also leave thecorresponding timestamps as-is in the past at step 509, so that dataaggregator 325 may resume from the last successful data aggregation forthe particular networked system when the networked system is repaired.At some point in the future when networked system 310D is repaired andbecomes available again, data aggregator 325 may aggregate the data froma time point preceding timepoint D 615 by predetermined overlap 607 tothe then current time point.

Once data aggregation is complete for all three categories of networkedsystems 310A-D, data aggregator 325 may wait until the next time point603 and repeat steps 501-509. Additionally or alternatively, reportgenerator 326 may, at step 510, generate data reports in response to thedata analysis queries in a manner similar to step 510 of FIG. 4.

While the present disclosure has been shown and described with referenceto particular embodiments thereof, it will be understood that thepresent disclosure can be practiced, without modification, in otherenvironments. The foregoing description has been presented for purposesof illustration. It is not exhaustive and is not limited to the preciseforms or embodiments disclosed. Modifications and adaptations will beapparent to those skilled in the art from consideration of thespecification and practice of the disclosed embodiments. Additionally,although aspects of the disclosed embodiments are described as beingstored in memory, one skilled in the art will appreciate that theseaspects can also be stored on other types of computer readable media,such as secondary storage devices, for example, hard disks or CD ROM, orother forms of RAM or ROM, USB media, DVD, Blu-ray, or other opticaldrive media.

Computer programs based on the written description and disclosed methodsare within the skill of an experienced developer. Various programs orprogram modules can be created using any of the techniques known to oneskilled in the art or can be designed in connection with existingsoftware. For example, program sections or program modules can bedesigned in or by means of .Net Framework, .Net Compact Framework (andrelated languages, such as Visual Basic, C, etc.), Java, C++,Objective-C, HTML, HTML/AJAX combinations, XML, or HTML with includedJava applets.

Moreover, while illustrative embodiments have been described herein, thescope of any and all embodiments having equivalent elements,modifications, omissions, combinations (e.g., of aspects across variousembodiments), adaptations and/or alterations as would be appreciated bythose skilled in the art based on the present disclosure. Thelimitations in the claims are to be interpreted broadly based on thelanguage employed in the claims and not limited to examples described inthe present specification or during the prosecution of the application.The examples are to be construed as non-exclusive. Furthermore, thesteps of the disclosed methods may be modified in any manner, includingby reordering steps and/or inserting or deleting steps. It is intended,therefore, that the specification and examples be considered asillustrative only, with a true scope and spirit being indicated by thefollowing claims and their full scope of equivalents.

What is claimed is:
 1. A computer-implemented system for dynamicaggregation of data and minimization of data loss, the systemcomprising: a memory storing instructions; and at least one processorconfigured to execute the instructions for: aggregating information froma plurality of networked systems by: collecting a first set of data at afirst time point from the networked systems, the first set of datacomprising data associated with a predetermined period of time andcomprising one or more central variables that are included in dataassociated with more than one networked systems of the plurality ofnetworked systems and one or more associated variables that describe oneor more aspects of the central variables, each of the central variablesand the associated variables comprising a corresponding value;retrieving one or more data transformation rules based on a relationalmap among the central variables and the associated variables;aggregating the first set of data into one or more master datastructures corresponding to the central variables based on the datatransformation rules, each of the one or more master data structurescomprising one or more data fields that correspond to one of the centralvariables and a subset of the associated variables; determining that asecond subset of the networked systems is available after a gap of time;and updating one or more timestamps corresponding to the networkedsystems based on the first time point; and generating one or more datareports based on the master data structures, wherein collecting thefirst set of data from the networked systems further comprisescollecting a second set of data from the second subset of the networkedsystems, the second set of data comprising data associated with the gapof time, and wherein aggregating the first set of data into the masterdata structures further comprises aggregating the second set of datainto the master data structures.
 2. The computer-implemented system ofclaim 1, wherein the data transformation rules define how a value in thefirst set of data corresponds to one or more data fields of the masterdata structures.
 3. The computer-implemented system of claim 1, whereina subset of the data transformation rules assigns values of one or moreof the associated variables to more than one data field of the masterdata structures.
 4. The computer-implemented system of claim 1, whereinthe processor is configured to repeat the instructions for aggregatinginformation from the networked systems at a predetermined interval,wherein the predetermined interval is less than or equal to thepredetermined period.
 5. The computer-implemented system of claim 4,wherein the predetermined interval is equal to or less than one minute.6. The computer-implemented system of claim 1, wherein aggregating theinformation from the networked systems further comprises: determiningthat a first subset of the networked systems is unavailable; andupdating one or more timestamps corresponding to the networked systems,wherein aggregating the first set of data into the master datastructures omits a subset of the data transformation rules thatcorrespond to a subset of data corresponding to the first subset of thenetworked systems, and wherein updating the timestamps omits updatingtimestamps that correspond to the first subset of the networked systems.7. The computer-implemented system of claim 1, wherein aggregating thefirst set of data comprises: sorting the first set of data in sequencebased on time; iterating through the sorted first set of datachronologically to replace existing values of one or more data fields inthe master data structures with the values from the sorted first set ofdata.
 8. The computer-implemented system of claim 1, wherein therelational map is determined based on one or more data profiles of thenetworked systems, the data profiles comprising at least one of one ormore metadata, one or more definitions of the associated variables, anda data element synonym registry.
 9. The computer-implemented system ofclaim 1, wherein generating the data reports comprises analyzing themaster data structures to determine one or more performance metricsassociated with the networked systems.
 10. A computer-implemented methodfor dynamic aggregation of data and minimization of data loss, themethod comprising: aggregating information from a plurality of networkedsystems by: collecting a first set of data at a first time point fromthe networked systems, the first set of data comprising data associatedwith a predetermined period of time and comprising one or more centralvariables that are included in data associated with more than onenetworked systems of the plurality of networked systems and one or moreassociated variables that describe one or more aspects of the centralvariables, each of the central variables and the associated variablescomprising a corresponding value; retrieving one or more datatransformation rules based on a relational map among the centralvariables and the associated variables; aggregating the first set ofdata into one or more master data structures corresponding to thecentral variables based on the data transformation rules, each of theone or more master data structures comprising one or more data fieldsthat correspond to one of the central variables and a subset of theassociated variables; determining that a second subset of the networkedsystems is available after a gap of time; and updating one or moretimestamps corresponding to the networked systems based on the firsttime point; and generating one or more data reports based on the masterdata structures, wherein collecting the first set of data from thenetworked systems further comprises collecting a second set of data fromthe second subset of the networked systems, the second set of datacomprising data associated with the gap of time, and wherein aggregatingthe first set of data into the master data structures further comprisesaggregating the second set of data into the master data structures. 11.The computer-implemented method of claim 10, wherein the datatransformation rules define how a value in the first set of datacorresponds to one or more data fields of the master data structures.12. The computer-implemented method of claim 10, wherein a subset of thedata transformation rules assigns values of one or more of theassociated variables to more than one data fields of the master datastructures.
 13. The computer-implemented method of claim 10, wherein theprocessor is configured to repeat the instructions for aggregatinginformation from the networked systems at a predetermined interval,wherein the predetermined interval is less than or equal to thepredetermined period.
 14. The computer-implemented method of claim 10,wherein aggregating the information from the networked systems furthercomprises: determining that a first subset of the networked systems isunavailable; and updating one or more timestamps corresponding to thenetworked systems, wherein aggregating the first set of data into themaster data structures omits a subset of the data transformation rulesthat correspond to a subset of data corresponding to the first subset ofthe networked systems, and wherein updating the timestamps omitsupdating timestamps that correspond to the first subset of the networkedsystems.
 15. The computer-implemented method of claim 10, whereinaggregating the first set of data comprises: sorting the first set ofdata in sequence based on time; iterating through the sorted first setof data chronologically to replace existing values of one or more datafields in the master data structures with the values from the sortedfirst set of data.
 16. The computer-implemented method of claim 10,wherein the relational map is determined based on one or more dataprofiles of the networked systems, the data profiles comprising at leastone of one or more metadata, one or more definitions of the associatedvariables, and a data element synonym registry.
 17. Thecomputer-implemented method of claim 10, wherein generating the datareports comprises analyzing the master data structures to determine oneor more performance metrics of the networked systems.
 18. Acomputer-implemented system for dynamic aggregation of data andminimization of data loss, the system comprising: a memory storinginstructions; and at least one processor configured to execute theinstructions for: aggregating information from a plurality of networkedsystems by: transmitting a data request to the networked systems at apredetermined interval; receiving a first set of data at a first timepoint from a first subset of networked systems, the first subset ofnetworked systems having a first set of corresponding timestamps from animmediately preceding time point, and the first set of data comprisingdata associated with a period of time between the immediately precedingtime point and the first time point; receiving a second set of data atthe first time point from a second subset of networked systems, thesecond subset of networked systems having a second set of correspondingtimestamps from a second time point older than the immediately precedingtime point, and the second set of data comprising data associated with aperiod between the second time point and the first time point; receivinga third set of data at the first time point from a third subset ofnetworked systems, the third subset of networked systems having a thirdset of corresponding timestamps from the immediately preceding timepoint, and the third set of data indicating that the third subset ofnetworked systems are not available, wherein the first set of data andthe second set of data comprise one or more central variables that areincluded in data from more than one networked systems of the pluralityof networked systems and one or more associated variables that describeone or more aspects of the central variables; retrieving one or moredata transformation rules based on a relational map among the centralvariables and the associated variables; aggregating the first and secondsets of data into one or more master data structures corresponding tothe central variables based on the data transformation rules, each ofthe one or more master data structures comprising one or more datafields that correspond to one of the central variables and a subset ofthe associated variables; and updating the first and second sets ofcorresponding timestamps based on the first time point; and generatingone or more data reports based on the master data structures.